CornerNet [1]} is a one-stage object detector that get over anchor boxes. It localizes object bounding boxes as a pair of key points, i.e., the top-left corner and the bottom-right corner, using a single cnn, predicting a heatmap for the top-left corners of all instances of the same object category, a heatmap for all bottom-right corners, and an embedding vector for each detected corner. This embedding vector, inspired by the associate embedding in [2]}, is such that if a top-left corner and a bottom-right corner belong to the same bounding box, the distance between their embeddings should be small. The actual values of the embeddings are unimportant. Only the distances between the embeddings are used to group the corners. Furthermore, the authors also introduced a novel component named corner pooling, a new type of pooling layer that helps a convolutional network better localize corners of bounding boxes.
